Minhye01 #2
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Minhye01 #2
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joowon-dm-snu
commented
May 4, 2024
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| # 주어진 문자열을 임베딩하는 함수 | ||
| def embed(text: str, model: str, wait_time: float = 0.1) -> list: |
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| # 주어진 프롬프트에 따라 답변을 생성하는 함수 | ||
| def generate(model: str, messages=list) -> str: |
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retry decorator
| user_question: str, | ||
| num_context: int, | ||
| min_sim_score: float, | ||
| ) -> Union[str, bool]: |
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| # 유저의 질문을 대답하기 위한 문맥을 찾는 함수 | ||
| def find_context( | ||
| vector_db: pd.DataFrame, |
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| user_vector = embed(user_question, model, wait_time=0) | ||
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| cosine_similarities = cosine_similarity( |
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| context = "" | ||
| for i in range(num_context): | ||
| context += f"\n Context {i+1} \n" | ||
| context += vector_db["QA"].iloc[top_indices[i]] |
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for -> list genetor
context = "~~~".join([ i + "..." for i in numbers])
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| import pandas as pd | |||
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chatbot/chatbot/chatbot.py 는 뭔가 어색하다
| import pandas as pd | ||
| import yaml | ||
| from exceptions import ExitReason | ||
| from LLM import generate |
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| # 답변에 활용할 문맥 검색 | ||
| context = find_context( | ||
| vector_db, |
| ) | ||
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| # 답변에 활용할 문맥 검색 | ||
| context = find_context( |
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# vector_database/base.py
class VectorDatabase(ABC):
def find_context():
raise NotImplementedError()
# vector_database/local.py
class PandasBasedVectorDatbase(VectorDatabase):
def find_context(..)
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